Structural cracks have serious repercussions on the safety, adaptability, and longevity of structures. Therefore, assessing cracks is an important parameter when evaluating the quality of concrete construction. As numerous cutting-edge automated inspection systems that exploit cracks have been developed, the necessity for individual/personal onsite inspection has reduced exponentially. However, these methods need to be improved in terms of cost efficiency and accuracy. The deep-learning-based assessment approaches for structural systems have seen a significant development noticed by the structural health monitoring (SHM) community. Convolutional neural networks (CNNs) are vital in these deep learning methods. Technologies such as convolutional neural networks hold promise for precise and accurate condition evaluation. Moreover, transfer learning enables users to use CNNs without needing a comprehensive grasp of algorithms or the capability to modify pre-trained networks for particular purposes. Within the context of this study, a thorough analysis of well-known pre-trained networks for classifying the cracks in buildings made of concrete is conducted. The classification performance of convolutional neural network designs such as VGG16, VGG19, ResNet 50, MobileNet, and Xception is compared to one another with the concrete crack image dataset. It is identified that the ResNet50-based classifier provided accuracy scores of 99.91% for training and 99.88% for testing. Xception architecture delivered the least performance, with training and test accuracy of 99.64% and 98.82%, respectively.

The addition of nanomaterials to concrete efficiently fills the pores of the concrete, thereby improving its hardening characteristics. However, no research is available in the literature that investigated the influence of nano-cement (NC), nano-silica-fume (NS), nano-fly-ash (NF), and nano-metakaolin (NM), which are used as partial replacements for cement, on the impact strength (IS) of concrete at elevated temperatures. This issue is addressed herein. Nanomaterials were used in this study to replace 10%, 20%, and 30% of the cement in four different grades of concrete, starting from M20 to M50, at different temperatures. This nano-blended matrix was exposed to various temperatures ranging from 250 °C to 1000 °C, with an increment of 250 °C. In total, the results of 384 new tests were reported. In addition, morphological changes undergone by the concrete specimens were observed through a scanning electron microscope (SEM). The study revealed that the type of binder, proportion of binder, heating intensity, duration, and cooling type directly influenced the impact strength of concrete when subjected to elevated temperature. In comparison to NC, NF, NS, and NM, the mix with NC possessed superior performance when it was heated at 1000 °C. Prior to being subjected to elevated temperatures, the MK blended concrete mix performed well; however, when subjected to elevated temperatures, the MK blended concrete also experienced severe damage.

Purpose: Health monitoring of concrete is one of the important tasks in the structural health monitoring. The life of any infrastructure relies on the quality of the concrete. The computer vision-based methods are very useful to identify the structural defects. The identification of minor cracks in the noisy concrete image is complex. The purpose of this paper is to denoise the concrete crack images and also segment the cracks. Design/methodology/approach: The novelty of the proposed work lies on the usage of anisotropic diffusion filter in the noisy concrete images. Initially anisotropic diffusion filter is applied to smoothen the concrete images. Adaptive threshold and gray level-based edge stopping constant are used in the diffusion process. The statistical six sigma-based method is utilized to segment the cracks from smoothened concrete images. Findings: The proposed method is compared with five state-of-the-art-methods with the performance metrics of mean square error, peak signal to noise ratio and mean structural similarity. The experimental results highlight the advantages of the proposed method. Originality/value: The novelty of the proposed work lies on the usage of anisotropic diffusion filter in the noisy concrete images. This research work gives the scope for structural damage evaluation by the automation techniques.